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.. _export_lstm_transducer_models_to_ncnn:
Export LSTM transducer models to ncnn
-------------------------------------
We use the pre-trained model from the following repository as an example:
`<https://huggingface.co/csukuangfj/icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03>`_
We will show you step by step how to export it to `ncnn`_ and run it with `sherpa-ncnn`_.
.. hint::
We use ``Ubuntu 18.04``, ``torch 1.13``, and ``Python 3.8`` for testing.
.. caution::
Please use a more recent version of PyTorch. For instance, ``torch 1.8``
may ``not`` work.
1. Download the pre-trained model
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. hint::
You have to install `git-lfs`_ before you continue.
.. code-block:: bash
cd egs/librispeech/ASR
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/csukuangfj/icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03
cd icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03
git lfs pull --include "exp/pretrained-iter-468000-avg-16.pt"
git lfs pull --include "data/lang_bpe_500/bpe.model"
cd ..
.. note::
We downloaded ``exp/pretrained-xxx.pt``, not ``exp/cpu-jit_xxx.pt``.
In the above code, we downloaded the pre-trained model into the directory
``egs/librispeech/ASR/icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03``.
2. Install ncnn and pnnx
^^^^^^^^^^^^^^^^^^^^^^^^
Please refer to :ref:`export_for_ncnn_install_ncnn_and_pnnx` .
3. Export the model via torch.jit.trace()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
First, let us rename our pre-trained model:
.. code-block::
cd egs/librispeech/ASR
cd icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp
ln -s pretrained-iter-468000-avg-16.pt epoch-99.pt
cd ../..
Next, we use the following code to export our model:
.. code-block:: bash
dir=./icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03
./lstm_transducer_stateless2/export-for-ncnn.py \
--exp-dir $dir/exp \
--bpe-model $dir/data/lang_bpe_500/bpe.model \
--epoch 99 \
--avg 1 \
--use-averaged-model 0 \
--num-encoder-layers 12 \
--encoder-dim 512 \
--rnn-hidden-size 1024
.. hint::
We have renamed our model to ``epoch-99.pt`` so that we can use ``--epoch 99``.
There is only one pre-trained model, so we use ``--avg 1 --use-averaged-model 0``.
If you have trained a model by yourself and if you have all checkpoints
available, please first use ``decode.py`` to tune ``--epoch --avg``
and select the best combination with with ``--use-averaged-model 1``.
.. note::
You will see the following log output:
.. literalinclude:: ./code/export-lstm-transducer-for-ncnn-output.txt
The log shows the model has ``84176356`` parameters, i.e., ``~84 M``.
.. code-block::
ls -lh icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/pretrained-iter-468000-avg-16.pt
-rw-r--r-- 1 kuangfangjun root 324M Feb 17 10:34 icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/pretrained-iter-468000-avg-16.pt
You can see that the file size of the pre-trained model is ``324 MB``, which
is roughly equal to ``84176356*4/1024/1024 = 321.107 MB``.
After running ``lstm_transducer_stateless2/export-for-ncnn.py``,
we will get the following files:
.. code-block:: bash
ls -lh icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/*pnnx.pt
-rw-r--r-- 1 kuangfangjun root 1010K Feb 17 11:22 icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/decoder_jit_trace-pnnx.pt
-rw-r--r-- 1 kuangfangjun root 318M Feb 17 11:22 icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/encoder_jit_trace-pnnx.pt
-rw-r--r-- 1 kuangfangjun root 3.0M Feb 17 11:22 icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/joiner_jit_trace-pnnx.pt
.. _lstm-transducer-step-4-export-torchscript-model-via-pnnx:
4. Export torchscript model via pnnx
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. hint::
Make sure you have set up the ``PATH`` environment variable
in :ref:`export_for_ncnn_install_ncnn_and_pnnx`. Otherwise,
it will throw an error saying that ``pnnx`` could not be found.
Now, it's time to export our models to `ncnn`_ via ``pnnx``.
.. code-block::
cd icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/
pnnx ./encoder_jit_trace-pnnx.pt
pnnx ./decoder_jit_trace-pnnx.pt
pnnx ./joiner_jit_trace-pnnx.pt
It will generate the following files:
.. code-block:: bash
ls -lh icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/*ncnn*{bin,param}
-rw-r--r-- 1 kuangfangjun root 503K Feb 17 11:32 icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/decoder_jit_trace-pnnx.ncnn.bin
-rw-r--r-- 1 kuangfangjun root 437 Feb 17 11:32 icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/decoder_jit_trace-pnnx.ncnn.param
-rw-r--r-- 1 kuangfangjun root 159M Feb 17 11:32 icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/encoder_jit_trace-pnnx.ncnn.bin
-rw-r--r-- 1 kuangfangjun root 21K Feb 17 11:32 icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/encoder_jit_trace-pnnx.ncnn.param
-rw-r--r-- 1 kuangfangjun root 1.5M Feb 17 11:33 icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/joiner_jit_trace-pnnx.ncnn.bin
-rw-r--r-- 1 kuangfangjun root 488 Feb 17 11:33 icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/joiner_jit_trace-pnnx.ncnn.param
There are two types of files:
- ``param``: It is a text file containing the model architectures. You can
use a text editor to view its content.
- ``bin``: It is a binary file containing the model parameters.
We compare the file sizes of the models below before and after converting via ``pnnx``:
.. see https://tableconvert.com/restructuredtext-generator
+----------------------------------+------------+
| File name | File size |
+==================================+============+
| encoder_jit_trace-pnnx.pt | 318 MB |
+----------------------------------+------------+
| decoder_jit_trace-pnnx.pt | 1010 KB |
+----------------------------------+------------+
| joiner_jit_trace-pnnx.pt | 3.0 MB |
+----------------------------------+------------+
| encoder_jit_trace-pnnx.ncnn.bin | 159 MB |
+----------------------------------+------------+
| decoder_jit_trace-pnnx.ncnn.bin | 503 KB |
+----------------------------------+------------+
| joiner_jit_trace-pnnx.ncnn.bin | 1.5 MB |
+----------------------------------+------------+
You can see that the file sizes of the models after conversion are about one half
of the models before conversion:
- encoder: 318 MB vs 159 MB
- decoder: 1010 KB vs 503 KB
- joiner: 3.0 MB vs 1.5 MB
The reason is that by default ``pnnx`` converts ``float32`` parameters
to ``float16``. A ``float32`` parameter occupies 4 bytes, while it is 2 bytes
for ``float16``. Thus, it is ``twice smaller`` after conversion.
.. hint::
If you use ``pnnx ./encoder_jit_trace-pnnx.pt fp16=0``, then ``pnnx``
won't convert ``float32`` to ``float16``.
5. Test the exported models in icefall
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. note::
We assume you have set up the environment variable ``PYTHONPATH`` when
building `ncnn`_.
Now we have successfully converted our pre-trained model to `ncnn`_ format.
The generated 6 files are what we need. You can use the following code to
test the converted models:
.. code-block:: bash
python3 ./lstm_transducer_stateless2/streaming-ncnn-decode.py \
--tokens ./icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/data/lang_bpe_500/tokens.txt \
--encoder-param-filename ./icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/encoder_jit_trace-pnnx.ncnn.param \
--encoder-bin-filename ./icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/encoder_jit_trace-pnnx.ncnn.bin \
--decoder-param-filename ./icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/decoder_jit_trace-pnnx.ncnn.param \
--decoder-bin-filename ./icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/decoder_jit_trace-pnnx.ncnn.bin \
--joiner-param-filename ./icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/joiner_jit_trace-pnnx.ncnn.param \
--joiner-bin-filename ./icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/joiner_jit_trace-pnnx.ncnn.bin \
./icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/test_wavs/1089-134686-0001.wav
.. hint::
`ncnn`_ supports only ``batch size == 1``, so ``streaming-ncnn-decode.py`` accepts
only 1 wave file as input.
The output is given below:
.. literalinclude:: ./code/test-streaming-ncnn-decode-lstm-transducer-libri.txt
Congratulations! You have successfully exported a model from PyTorch to `ncnn`_!
.. _lstm-modify-the-exported-encoder-for-sherpa-ncnn:
6. Modify the exported encoder for sherpa-ncnn
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
In order to use the exported models in `sherpa-ncnn`_, we have to modify
``encoder_jit_trace-pnnx.ncnn.param``.
Let us have a look at the first few lines of ``encoder_jit_trace-pnnx.ncnn.param``:
.. code-block::
7767517
267 379
Input in0 0 1 in0
**Explanation** of the above three lines:
1. ``7767517``, it is a magic number and should not be changed.
2. ``267 379``, the first number ``267`` specifies the number of layers
in this file, while ``379`` specifies the number of intermediate outputs
of this file
3. ``Input in0 0 1 in0``, ``Input`` is the layer type of this layer; ``in0``
is the layer name of this layer; ``0`` means this layer has no input;
``1`` means this layer has one output; ``in0`` is the output name of
this layer.
We need to add 1 extra line and also increment the number of layers.
The result looks like below:
.. code-block:: bash
7767517
268 379
SherpaMetaData sherpa_meta_data1 0 0 0=3 1=12 2=512 3=1024
Input in0 0 1 in0
**Explanation**
1. ``7767517``, it is still the same
2. ``268 379``, we have added an extra layer, so we need to update ``267`` to ``268``.
We don't need to change ``379`` since the newly added layer has no inputs or outputs.
3. ``SherpaMetaData sherpa_meta_data1 0 0 0=3 1=12 2=512 3=1024``
This line is newly added. Its explanation is given below:
- ``SherpaMetaData`` is the type of this layer. Must be ``SherpaMetaData``.
- ``sherpa_meta_data1`` is the name of this layer. Must be ``sherpa_meta_data1``.
- ``0 0`` means this layer has no inputs or output. Must be ``0 0``
- ``0=3``, 0 is the key and 3 is the value. MUST be ``0=3``
- ``1=12``, 1 is the key and 12 is the value of the
parameter ``--num-encoder-layers`` that you provided when running
``./lstm_transducer_stateless2/export-for-ncnn.py``.
- ``2=512``, 2 is the key and 512 is the value of the
parameter ``--encoder-dim`` that you provided when running
``./lstm_transducer_stateless2/export-for-ncnn.py``.
- ``3=1024``, 3 is the key and 1024 is the value of the
parameter ``--rnn-hidden-size`` that you provided when running
``./lstm_transducer_stateless2/export-for-ncnn.py``.
For ease of reference, we list the key-value pairs that you need to add
in the following table. If your model has a different setting, please
change the values for ``SherpaMetaData`` accordingly. Otherwise, you
will be ``SAD``.
+------+-----------------------------+
| key | value |
+======+=============================+
| 0 | 3 (fixed) |
+------+-----------------------------+
| 1 | ``--num-encoder-layers`` |
+------+-----------------------------+
| 2 | ``--encoder-dim`` |
+------+-----------------------------+
| 3 | ``--rnn-hidden-size`` |
+------+-----------------------------+
4. ``Input in0 0 1 in0``. No need to change it.
.. caution::
When you add a new layer ``SherpaMetaData``, please remember to update the
number of layers. In our case, update ``267`` to ``268``. Otherwise,
you will be SAD later.
.. hint::
After adding the new layer ``SherpaMetaData``, you cannot use this model
with ``streaming-ncnn-decode.py`` anymore since ``SherpaMetaData`` is
supported only in `sherpa-ncnn`_.
.. hint::
`ncnn`_ is very flexible. You can add new layers to it just by text-editing
the ``param`` file! You don't need to change the ``bin`` file.
Now you can use this model in `sherpa-ncnn`_.
Please refer to the following documentation:
- Linux/macOS/Windows/arm/aarch64: `<https://k2-fsa.github.io/sherpa/ncnn/install/index.html>`_
- ``Android``: `<https://k2-fsa.github.io/sherpa/ncnn/android/index.html>`_
- ``iOS``: `<https://k2-fsa.github.io/sherpa/ncnn/ios/index.html>`_
- Python: `<https://k2-fsa.github.io/sherpa/ncnn/python/index.html>`_
We have a list of pre-trained models that have been exported for `sherpa-ncnn`_:
- `<https://k2-fsa.github.io/sherpa/ncnn/pretrained_models/index.html>`_
You can find more usages there.
7. (Optional) int8 quantization with sherpa-ncnn
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
This step is optional.
In this step, we describe how to quantize our model with ``int8``.
Change :ref:`lstm-transducer-step-4-export-torchscript-model-via-pnnx` to
disable ``fp16`` when using ``pnnx``:
.. code-block::
cd icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/
pnnx ./encoder_jit_trace-pnnx.pt fp16=0
pnnx ./decoder_jit_trace-pnnx.pt
pnnx ./joiner_jit_trace-pnnx.pt fp16=0
.. note::
We add ``fp16=0`` when exporting the encoder and joiner. `ncnn`_ does not
support quantizing the decoder model yet. We will update this documentation
once `ncnn`_ supports it. (Maybe in this year, 2023).
.. code-block:: bash
ls -lh icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/*_jit_trace-pnnx.ncnn.{param,bin}
-rw-r--r-- 1 kuangfangjun root 503K Feb 17 11:32 icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/decoder_jit_trace-pnnx.ncnn.bin
-rw-r--r-- 1 kuangfangjun root 437 Feb 17 11:32 icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/decoder_jit_trace-pnnx.ncnn.param
-rw-r--r-- 1 kuangfangjun root 317M Feb 17 11:54 icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/encoder_jit_trace-pnnx.ncnn.bin
-rw-r--r-- 1 kuangfangjun root 21K Feb 17 11:54 icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/encoder_jit_trace-pnnx.ncnn.param
-rw-r--r-- 1 kuangfangjun root 3.0M Feb 17 11:54 icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/joiner_jit_trace-pnnx.ncnn.bin
-rw-r--r-- 1 kuangfangjun root 488 Feb 17 11:54 icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/joiner_jit_trace-pnnx.ncnn.param
Let us compare again the file sizes:
+----------------------------------------+------------+
| File name | File size |
+----------------------------------------+------------+
| encoder_jit_trace-pnnx.pt | 318 MB |
+----------------------------------------+------------+
| decoder_jit_trace-pnnx.pt | 1010 KB |
+----------------------------------------+------------+
| joiner_jit_trace-pnnx.pt | 3.0 MB |
+----------------------------------------+------------+
| encoder_jit_trace-pnnx.ncnn.bin (fp16) | 159 MB |
+----------------------------------------+------------+
| decoder_jit_trace-pnnx.ncnn.bin (fp16) | 503 KB |
+----------------------------------------+------------+
| joiner_jit_trace-pnnx.ncnn.bin (fp16) | 1.5 MB |
+----------------------------------------+------------+
| encoder_jit_trace-pnnx.ncnn.bin (fp32) | 317 MB |
+----------------------------------------+------------+
| joiner_jit_trace-pnnx.ncnn.bin (fp32) | 3.0 MB |
+----------------------------------------+------------+
You can see that the file sizes are doubled when we disable ``fp16``.
.. note::
You can again use ``streaming-ncnn-decode.py`` to test the exported models.
Next, follow :ref:`lstm-modify-the-exported-encoder-for-sherpa-ncnn`
to modify ``encoder_jit_trace-pnnx.ncnn.param``.
Change
.. code-block:: bash
7767517
267 379
Input in0 0 1 in0
to
.. code-block:: bash
7767517
268 379
SherpaMetaData sherpa_meta_data1 0 0 0=3 1=12 2=512 3=1024
Input in0 0 1 in0
.. caution::
Please follow :ref:`lstm-modify-the-exported-encoder-for-sherpa-ncnn`
to change the values for ``SherpaMetaData`` if your model uses a different setting.
Next, let us compile `sherpa-ncnn`_ since we will quantize our models within
`sherpa-ncnn`_.
.. code-block:: bash
# We will download sherpa-ncnn to $HOME/open-source/
# You can change it to anywhere you like.
cd $HOME
mkdir -p open-source
cd open-source
git clone https://github.com/k2-fsa/sherpa-ncnn
cd sherpa-ncnn
mkdir build
cd build
cmake ..
make -j 4
./bin/generate-int8-scale-table
export PATH=$HOME/open-source/sherpa-ncnn/build/bin:$PATH
The output of the above commands are:
.. code-block:: bash
(py38) kuangfangjun:build$ generate-int8-scale-table
Please provide 10 arg. Currently given: 1
Usage:
generate-int8-scale-table encoder.param encoder.bin decoder.param decoder.bin joiner.param joiner.bin encoder-scale-table.txt joiner-scale-table.txt wave_filenames.txt
Each line in wave_filenames.txt is a path to some 16k Hz mono wave file.
We need to create a file ``wave_filenames.txt``, in which we need to put
some calibration wave files. For testing purpose, we put the ``test_wavs``
from the pre-trained model repository
`<https://huggingface.co/csukuangfj/icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03>`_
.. code-block:: bash
cd egs/librispeech/ASR
cd icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/
cat <<EOF > wave_filenames.txt
../test_wavs/1089-134686-0001.wav
../test_wavs/1221-135766-0001.wav
../test_wavs/1221-135766-0002.wav
EOF
Now we can calculate the scales needed for quantization with the calibration data:
.. code-block:: bash
cd egs/librispeech/ASR
cd icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/
generate-int8-scale-table \
./encoder_jit_trace-pnnx.ncnn.param \
./encoder_jit_trace-pnnx.ncnn.bin \
./decoder_jit_trace-pnnx.ncnn.param \
./decoder_jit_trace-pnnx.ncnn.bin \
./joiner_jit_trace-pnnx.ncnn.param \
./joiner_jit_trace-pnnx.ncnn.bin \
./encoder-scale-table.txt \
./joiner-scale-table.txt \
./wave_filenames.txt
The output logs are in the following:
.. literalinclude:: ./code/generate-int-8-scale-table-for-lstm.txt
It generates the following two files:
.. code-block:: bash
ls -lh encoder-scale-table.txt joiner-scale-table.txt
-rw-r--r-- 1 kuangfangjun root 345K Feb 17 12:13 encoder-scale-table.txt
-rw-r--r-- 1 kuangfangjun root 17K Feb 17 12:13 joiner-scale-table.txt
.. caution::
Definitely, you need more calibration data to compute the scale table.
Finally, let us use the scale table to quantize our models into ``int8``.
.. code-block:: bash
ncnn2int8
usage: ncnn2int8 [inparam] [inbin] [outparam] [outbin] [calibration table]
First, we quantize the encoder model:
.. code-block:: bash
cd egs/librispeech/ASR
cd icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/
ncnn2int8 \
./encoder_jit_trace-pnnx.ncnn.param \
./encoder_jit_trace-pnnx.ncnn.bin \
./encoder_jit_trace-pnnx.ncnn.int8.param \
./encoder_jit_trace-pnnx.ncnn.int8.bin \
./encoder-scale-table.txt
Next, we quantize the joiner model:
.. code-block:: bash
ncnn2int8 \
./joiner_jit_trace-pnnx.ncnn.param \
./joiner_jit_trace-pnnx.ncnn.bin \
./joiner_jit_trace-pnnx.ncnn.int8.param \
./joiner_jit_trace-pnnx.ncnn.int8.bin \
./joiner-scale-table.txt
The above two commands generate the following 4 files:
.. code-block::
-rw-r--r-- 1 kuangfangjun root 218M Feb 17 12:19 encoder_jit_trace-pnnx.ncnn.int8.bin
-rw-r--r-- 1 kuangfangjun root 21K Feb 17 12:19 encoder_jit_trace-pnnx.ncnn.int8.param
-rw-r--r-- 1 kuangfangjun root 774K Feb 17 12:19 joiner_jit_trace-pnnx.ncnn.int8.bin
-rw-r--r-- 1 kuangfangjun root 496 Feb 17 12:19 joiner_jit_trace-pnnx.ncnn.int8.param
Congratulations! You have successfully quantized your model from ``float32`` to ``int8``.
.. caution::
``ncnn.int8.param`` and ``ncnn.int8.bin`` must be used in pairs.
You can replace ``ncnn.param`` and ``ncnn.bin`` with ``ncnn.int8.param``
and ``ncnn.int8.bin`` in `sherpa-ncnn`_ if you like.
For instance, to use only the ``int8`` encoder in ``sherpa-ncnn``, you can
replace the following invocation:
.. code-block::
cd egs/librispeech/ASR
cd icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/
sherpa-ncnn \
../data/lang_bpe_500/tokens.txt \
./encoder_jit_trace-pnnx.ncnn.param \
./encoder_jit_trace-pnnx.ncnn.bin \
./decoder_jit_trace-pnnx.ncnn.param \
./decoder_jit_trace-pnnx.ncnn.bin \
./joiner_jit_trace-pnnx.ncnn.param \
./joiner_jit_trace-pnnx.ncnn.bin \
../test_wavs/1089-134686-0001.wav
with
.. code-block:: bash
cd egs/librispeech/ASR
cd icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/
sherpa-ncnn \
../data/lang_bpe_500/tokens.txt \
./encoder_jit_trace-pnnx.ncnn.int8.param \
./encoder_jit_trace-pnnx.ncnn.int8.bin \
./decoder_jit_trace-pnnx.ncnn.param \
./decoder_jit_trace-pnnx.ncnn.bin \
./joiner_jit_trace-pnnx.ncnn.param \
./joiner_jit_trace-pnnx.ncnn.bin \
../test_wavs/1089-134686-0001.wav
The following table compares again the file sizes:
+----------------------------------------+------------+
| File name | File size |
+----------------------------------------+------------+
| encoder_jit_trace-pnnx.pt | 318 MB |
+----------------------------------------+------------+
| decoder_jit_trace-pnnx.pt | 1010 KB |
+----------------------------------------+------------+
| joiner_jit_trace-pnnx.pt | 3.0 MB |
+----------------------------------------+------------+
| encoder_jit_trace-pnnx.ncnn.bin (fp16) | 159 MB |
+----------------------------------------+------------+
| decoder_jit_trace-pnnx.ncnn.bin (fp16) | 503 KB |
+----------------------------------------+------------+
| joiner_jit_trace-pnnx.ncnn.bin (fp16) | 1.5 MB |
+----------------------------------------+------------+
| encoder_jit_trace-pnnx.ncnn.bin (fp32) | 317 MB |
+----------------------------------------+------------+
| joiner_jit_trace-pnnx.ncnn.bin (fp32) | 3.0 MB |
+----------------------------------------+------------+
| encoder_jit_trace-pnnx.ncnn.int8.bin | 218 MB |
+----------------------------------------+------------+
| joiner_jit_trace-pnnx.ncnn.int8.bin | 774 KB |
+----------------------------------------+------------+
You can see that the file size of the joiner model after ``int8`` quantization
is much smaller. However, the size of the encoder model is even larger than
the ``fp16`` counterpart. The reason is that `ncnn`_ currently does not support
quantizing ``LSTM`` layers into ``8-bit``. Please see
`<https://github.com/Tencent/ncnn/issues/4532>`_
.. hint::
Currently, only linear layers and convolutional layers are quantized
with ``int8``, so you don't see an exact ``4x`` reduction in file sizes.
.. note::
You need to test the recognition accuracy after ``int8`` quantization.
That's it! Have fun with `sherpa-ncnn`_!
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